Abstract
It would be a mistake to think that machine learning always requires examples with class labels. Far from it! Useful information can be gleaned even from examples whose classes are not known. This is sometimes called unsupervised learning, in contrast to the term supervised learning which is used when talking about induction from pre-classified examples.
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Notes
- 1.
Machine learning professionals sometimes avoid the term “center” which might imply mathematical properties that are for the specific needs of cluster analysis largely irrelevant.
- 2.
For instance, the section on RBF networks denoted these centers by μ i ’s.
- 3.
In statistics, and in neural networks, scientists often use the term feature instead of attribute.
- 4.
Recall that one epoch means that all training examples have been presented once.
- 5.
A bulldozer is more powerful than a spade, and yet the gardener prefers the spade most of the time.
References
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McQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the 5th Berkeley symposium on mathematical statistics and probability, Berkeley (pp. 281–297).
Murty, M. N. & Krishna, G. (1980). A computationally efficient technique for data clustering. Pattern Recognition, 12, 153–158.
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Kubat, M. (2017). Unsupervised Learning. In: An Introduction to Machine Learning. Springer, Cham. https://doi.org/10.1007/978-3-319-63913-0_14
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DOI: https://doi.org/10.1007/978-3-319-63913-0_14
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